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Tong Liu

Publications and source records attributed to Tong Liu.

At least 37 records · Page 2Linked to original sources
arXiv2026

Fine-tuning an ECG Foundation Model to Predict Coronary CT Angiography Outcomes

Coronary artery disease (CAD) remains a major global public health burden, yet scalable pre-imaging risk stratification tools are limited. In this multicenter study, we developed and validated an artificial intelligence-enabled electrocardiography (AI-ECG) model using coronary computed tomographic angiography (CCTA) as the anatomical reference to predict vessel-specific hemodynamically significant stenosis ($\geq 70\%$ for RCA, LAD, LCX; $\geq 50\%$ for LM). The model was evaluated in internal and external cohorts, clinically normal ECGs, and prespecified demographic and clinical subgroups. It showed discrimination across vessels in internal validation and consistent external and normal ECG performance. Predicted probabilities increased with CCTA-defined stenosis severity and were converted into vessel-specific low-, intermediate-, and high-risk strata. Calibration and decision curve analyses supported its clinical utility. Integration with guideline-based pre-test probability improved risk reclassification, enhanced rule-out performance, and reduced the gray-zone proportion. In longitudinal follow-up, model-defined risk groups showed clear separation in major adverse cardiovascular events. Waveform- and attribution-based analyses identified structured ECG differences and physiologically meaningful signal regions linked to high-risk predictions. These results support AI-ECG as a feasible tool for pre-imaging risk stratification and clinical triage, warranting prospective validation in broader clinical settings.

cs.CV↗
arXiv2026

The Continuum Model for Uniaxially Strained Bilayer Graphene Moiré Systems

We construct a continuum model for a one-dimensional moiré superlattice formed by stretching one layer of AB-stacked bilayer graphene along the x direction by a factor s. Following the spirit of the Bistritzer-MacDonald model for twisted bilayer graphene, we treat the interlayer coupling as hopping between several Dirac points. At a critical stretch factor s ~ 1.018 the two bands near the Fermi level touch, forming two degeneracy points along the k_y direction. This gap closing is accompanied by a topological phase transition, in which the Chern number changes from 1 to -1, and by a sign change of the Berry-curvature dipole, which we propose can be detected through the nonlinear Hall effect. We find that uniaxial strain modulates inter-Dirac-valley coupling, which drives band gap collapse and subsequent topological number inversion. This opens a route to engineer topological transport and quantum anomalous Hall effects via strain engineering of moiré heterostructures.

cond-mat.mes-hall↗
arXiv2026

Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface

In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning. To bridge this gap, we propose the Multi-feature Riemannian Hypergraph (MRieHy), a framework tailored for online test-time adaptation in MI-BCI decoding that leverages Riemannian geometry to strengthen cross-day transferability. MRieHy first computes Riemannian means of covariance matrices from cross-day training data to align multi-day distributions. It then constructs a hypergraph over covariance matrices using Riemannian distance, complemented by a second hypergraph over deep features built with cosine similarity. The two hypergraphs are fused via adaptively learned combination weights, jointly optimized with the label projection matrices. During online testing, MRieHy maintains a first-in-first-out buffer of recent samples, performs Riemannian alignment on the buffered data, and decodes with the learned hypergraph. Extensive experiments on a private four-class ECoG dataset and two public four-class EEG datasets validate that MRieHy achieves notable performance gains over state-of-the-art baselines.

cs.LG↗
arXiv2026

A statistical relation of energy injection plateaus in multi-band afterglows of gamma-ray bursts

The origin of the plateau phase in gamma-ray burst (GRB) afterglows remains under debate, with the energy injection model being one of the most competitive explanations. If the plateau is truly driven by energy injection, the average X-ray and optical luminosities during the plateau phase, $L_{\rm X, plat, ave}$ and $L_{\rm opt, plat, ave}$, should naturally be correlated. Moreover, under this scenario, the scaling relations between the luminosities during the plateau and the normal decay phase are expected to be consistent since they share the same origin, i.e., synchrotron radiation from the external forward shock. Therefore, simultaneous multi-band observations are essential to verify this mechanism. In this work, we select a sample of 47 GRBs with simultaneous plateaus in both bands. We calculate their time-averaged isotropic luminosities for the plateau and the subsequent normal decay phases. We find a moderate positive correlation $\log L_{\rm X, plat, ave}=m\log L_{\rm opt, plat, ave}+c$ with a slope $m = 0.86 \pm 0.11$ for the plateau phase. This correlation supports the energy injection origin and offers a promising diagnostic approach to test the model. Furthermore, we obtain a similar slope $m = 1.05 \pm 0.05$ for the normal decay phase, which reinforces the idea that both phases share the same physical origin. Notably, the post-plateau data exhibit a systematic downward shift in luminosity, which may indicate the cessation of the central engine.

astro-ph.HE↗
arXiv2026

Impact of Neutrino Flavour Conversion on the Diffuse Neutrino Background from Neutrino-dominated Accretion Flows

Neutrino-dominated accretion flows (NDAFs) are believed to form during the fallback accretion phase of some core-collapse supernovae (CCSNe). Such systems produce copious neutrino emission, whose cumulative contribution over cosmic history forms the diffuse NDAF neutrino background (DNNB). As neutrinos propagate from the source to Earth, flavour conversion can significantly modify the observed neutrino spectra and consequently the detectability of the DNNB. In this work, based on fallback CCSN simulations, we investigate the effects of progenitor mass, metallicity, and initial explosion energy on neutrino emission from NDAFs. We calculate the heavy-lepton neutrino ($ν_x$) spectra from NDAFs and incorporate them into DNNB predictions. We find that the unoscillated $ν_x$ spectra are more than an order of magnitude lower than those of electron antineutrinos $\barν_e$. Using the latest neutrino oscillation parameters reported by the Jiangmen Underground Neutrino Observatory (JUNO), we evaluate the impact of flavour conversion on the DNNB and derive the corresponding spectra for both the normal and inverted mass orderings. We further estimate the expected event numbers in JUNO and Hyper-Kamiokande. We find that the predicted DNNB signal is strongly dependent on the neutrino mass ordering. While the DNNB may be detectable in the normal ordering with next-generation neutrino detectors, the signal is significantly suppressed in the inverted ordering, making detection considerably more challenging.

astro-ph.HE↗
arXiv2026

On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This paper studies tool-calling along two complementary axes: effectiveness, i.e., how this capability is measured, and efficiency, i.e., how it is learned. On effectiveness, we systematically analyze tool-calling evaluation pipelines and show that results can be highly sensitive to seemingly minor, often undocumented implementation choices including the random seed, system prompt, multi-turn template construction, and how prior interaction/reasoning history is carried forward. These choices can lead to substantial differences in reported performance, especially in multi-turn settings where without rigorous standardization, leaderboard rankings are unreliable. On efficiency, we examine standard reinforcement learning (RL) for tool-calling and identify two sources of computational waste: (i) during rollouts, many prompts produce no learning signal, and (ii) during policy updates, optimization incurs high computational cost. Guided by these findings, we introduce two techniques that accelerate RL-based tool-calling training, achieving substantial wall-clock speedup without degrading performance.

cs.LG↗
arXiv2026

Impact of disk magnetic fields on the propagation of stellar-scale jets in the magnetically arrested accretion disks of active galactic nuclei

It is widely recognized that active galactic nucleus (AGN) disks host numerous massive stars and compact objects. Stellar-scale jets triggered by collapses of massive stars and mergers of compact objects could propagate through the disk and produce observable electromagnetic radiation. Magnetically arrested disks (MADs), supported by both numerical simulations and observations, possess strong magnetic fields (MFs). As jets travel within such environments, the MFs should regulate jet evolution and shape radiation signatures. In this work, we explore the effects of disk MFs on jet propagation and breakout emission within the MAD framework. We employ a jet-cocoon model that accounts for potential disk-MF effects, including both magnetic pressure and magnetic energy dissipation driven by magnetic reconnection. We find that magnetic pressure effectively suppresses the lateral expansion of the cocoon, which enhances jet collimation and modestly increases the jet-head velocity. Furthermore, magnetic pressure effects are more pronounced at relatively low jet powers. In this regime, the breakout luminosity of the jet-head shock is enhanced, while its breakout time is shortened. However, the magnitude of the luminosity enhancement is sensitive to the adopted regime-dependent emission prescriptions. These findings suggest that, within the explored parameter space, disk MFs can facilitate the breakout of low-power jets arising from binary black hole mergers in AGN MADs.

astro-ph.HE↗
arXiv2026

A Parameter-Specific Retrieval and Knowledge-Guided Reasoning Framework for LLM-Based GPSR Optimization in FANETs

Existing Greedy Perimeter Stateless Routing (GPSR)-based protocols for Flying Ad-Hoc Networks (FANETs) struggle to adapt routing parameters, such as hello interval, multi-path number, and greedy forwarding weights, under highly dynamic environments. As an emerging artificial intelligence technology, large language models (LLMs) show potential for intelligent decision-making, providing new opportunities for adaptive adjustment of GPSR parameters to improve network performance. However, applying LLMs to GPSR remains challenging due to irrelevant experience retrieval and the absence of protocol constraints. To address these issues, we propose a Parameter-Specific Multi-Index Retrieval and Knowledge-Guided Reasoning framework for adaptive GPSR optimization (PMKR-GPSR), an LLM-based framework that enables protocol-consistent routing parameter adaptation. We design a parameter-specific multi-index retrieval mechanism to provide LLMs with parameter-relevant experiences while reducing interference from irrelevant information. We further construct a knowledge-guided constraint graph to enforce that the routing parameters satisfy dependency rules and optimization constraints. Simulation results demonstrate that PMKR-GPSR achieves higher packet delivery ratio and lower end-to-end delay under high-mobility FANETs.

cs.NI↗
arXiv2026

Capacity Markets for Large Loads under Supply-Chain Constraints

Motivated by the rapid growth of data centers, we develop a model to evaluate bringyour-own-capacity (BYOC) mandates and flexibility accreditation in capacity markets for new large loads with shared supply-chain constraints. With efficient pricing, BYOC mainly reallocates procurement between grid-built and self-built capacity and therefore has little welfare effect, while flexibility delivers a modest gain by reducing the effective capacity requirement. Under administrative price caps, mandates can improve static welfare by forcing data centers to internalize the full cost of capacity. The welfare ranking of the two instruments depends on supply-chain stress. At low or moderate stress, only the flexibility instrument raises welfare. Under severe stress with capped prices, the welfare gain from the BYOC obligation can exceed the gross flexibility benefit. The two instruments differ in their effects on a neighboring market: a unilateral BYOC mandate can crowd out its capacity investment, while flexibility produces essentially no spillover at our calibrated benchmark. Finally, applying current capacity non-performance penalties to flexible loads may lead to financial incentives that are too weak to induce truthful flexibility reporting.

eess.SY↗
arXiv2026

Emergent Replica Clock Unifies Many-Body Localization and Thermalization

Many-body localization (MBL) and eigenstate thermalization (ETH) are traditionally distinguished by a collection of separate diagnostics, not by a single order parameter. We show that replication and folding of isolated unitary dynamics generate a complex statistical mechanics of forward--backward history pairings and, in a closed cyclic infrared sector, an emergent clock $r_x\in\mathbb Z_t$. Its correlator has three distinct asymptotics: exponential decay in MBL, scale-free decay at criticality, and long-range locking in the thermal phase. A controlled $l$-bit reduction makes the clock action quasi-local, while its inverse correlation length obeys $ξ_{\rm pair}^{-1}=κ_{\rm el}=\ln|ρ_0/ρ_q|$, unifying history coherence, the defect line tension, and the transfer spectrum. Replica order supplies a second, discrete coordinate---the first order at which a hidden dynamical invariant becomes visible---and can distinguish localized dynamics that share the same spatial correlation length.

cond-mat.dis-nn↗
arXiv2026

Relativistic formulation for dual one-way Doppler Cancellation Scheme observables for gravitational redshift tests

Gravitational redshift is a fundamental prediction of general relativity and a sensitive probe of possible deviations from it. Motivated by recent progress in optical clocks and satellite-ground frequency transfer, we study a dual one-way optical Doppler-cancellation observable for space-based gravitational-redshift tests. The corresponding observable is constructed by combining oppositely directed one-way frequency observables, namely an uplink and a downlink whose measurements are recorded at the receiving terminals and combined in post-processing. We derive the corresponding relativistic observable up to order $c^{-3}$, as required for future $10^{-18}$-level clock comparisons. The resulting combination suppresses the first-order Doppler contribution to second order while retaining the gravitational-redshift signal. We analyze the dominant residual effects, including higher-order Doppler terms, atmospheric delay, Shpiro delay, tidal effects, clock synchronization etc. The formalism is applied to representative satellite-ground configurations, including an ACES/CSS-like low-Earth-orbit configuration and a geostationary Earth-orbit satellite. The results show that the dual one-way optical DCS configuration provides a useful relativistic framework and error-modeling reference for future $10^{-18}$-level space-based gravitational-redshift tests, while highlighting the need for stringent clock synchronization.

gr-qc↗
arXiv2026

LocAnyMed: Vision-Language Grounding for Multimodal Medical Images

Medical visual grounding connects free-form clinical queries to spatial evidence in medical images and is an important component of interpretable medical artificial intelligence. However, general-purpose grounding models are predominantly trained on natural images, while existing medical localization resources remain fragmented across imaging modalities, datasets, and task formulations. To address this gap, we construct LocAnyMed-200K, a multimodal medical visual grounding dataset containing approximately 200K image-query-answer examples across computed tomography, optical medical imaging, ultrasound, and X-ray. We harmonize heterogeneous detection and localization resources into a unified free-form instruction format that supports one or multiple bounding boxes, point coordinates, and no-target outputs for negative queries. Full-parameter fine-tuning of LocateAnything-3B on LocAnyMed-200K improves F1@IoU 0.50 from 10.64 to 85.59 on a held-out evaluation split, demonstrating that large-scale domain-specific supervision can equip a general grounding model with effective medical localization capabilities. Beyond spatial coordinates, a clinically interpretable grounding system should also communicate the evidence supporting its prediction. We therefore derive LocAnyMed-CoT-20K, a rationale-augmented subset that connects anatomical context, visual observations, and spatial conclusions through structured reasoning and further improves cross-source generalization through fine-tuning. Together, these resources provide a unified foundation for studying both localization accuracy and rationale quality across heterogeneous medical imaging modalities. The code is publicly available at https://github.com/MiliLab/LocAnyMed.

cs.CV↗
arXiv2026

Reincarnations of massive stars in active galactic nucleus discs

The origin and evolution of massive stars in active galactic nucleus (AGN) discs remain uncertain. We develop a semi-analytical model that follows the evolution of an embedded core-collapse supernova (CCSN) remnant and the subsequent formation and growth of a compact gas cloud. In the dense disc environment, efficient radiative cooling can strongly compress or bypass the Sedov-Taylor stage and drive the remnant rapidly into radiative snowplow evolution. After the remnant loses its interior pressure support, partial backflow of cooled shell fragments and refilling gas may initialize a pressure-confined and tidally limited seed cloud. The cloud then grows through shear-limited Hill capture until the gravitational, tidal, shear, photoionization, and magnetic conditions for collapse are simultaneously satisfied. The outcome depends strongly on the supermassive black-hole (SMBH) mass and explosion radius. Models with the lowest SMBH mass yield fewer than one massive star per event on average, whereas the most massive SMBH models can produce from several to several hundred. For a top-heavy initial mass function, massive stars dominate the resulting stellar mass and feedback budget. Embedded supernovae may therefore provide a localized gas-recycling channel for second-generation massive-star formation in AGN discs.

astro-ph.GA↗
arXiv2026

Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers

Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RLVR), particularly in domains like mathematics and programming, where ground-truth correctness can be automatically evaluated. However, extending this success to other reasoning-intensive domains remains challenging due to the scarcity of high-quality, verifiable datasets and the high cost of human supervision. In this work, we introduce the Loong Project: an open-source framework for scalable synthetic data generation and verification across a diverse range of reasoning-intensive domains. The framework consists of two key components: (1) LoongBench, a curated seed dataset containing 8,729 human-vetted examples across 12 domains (e.g., Advanced Mathematics, Chemistry, Logic), each paired with executable code and rich metadata; and (2) LoongEnv, a modular synthetic data generation environment that supports multiple prompting strategies to produce new question-answer-code triples. Together, these components form an agent-environment loop that enables reinforcement learning, where an LLM-based agent is rewarded for generating Chain-of-Thought (CoT) solutions that align with code-executed answers. Empirically, we benchmark LoongBench on a broad suite of both open-source and proprietary LLMs to evaluate domain coverage and reveal performance bottlenecks. In addition, we conduct a comprehensive analysis of synthetic data generated by LoongEnv, examining correctness, difficulty, and diversity. Code and documentation are available at https://github.com/camel-ai/loong.

cs.LG↗
arXiv2026

Distinct reentrant transitions in a quasi-periodic Raman lattice

We investigate a one-dimensional lattice with spin-orbit coupling (SOC) and a Zeeman potential containing uniform and quasiperiodic components. By tuning SOC, anomalous mobility edges emerge that separate critical from non-critical states, yielding a reentrant transition between two mixed phases, M$_1$$\to$M$_2$$\to$M$_1$, where M$_1$ (M$_2$) lacks (hosts) anomalous mobility edges. A new \emph{reentrant criticality transition}, defined as multiple entries into the critical phase, is identified. As a counterpart to reentrant delocalization/localization transitions, it completes the basic framework of reentrant phenomena across extended, localized, and critical states. The uniform Zeeman potential drives a reentrant delocalization transition, arising from the splitting of the localized region induced by the shift of mobility edges. This reveals a distinct pathway for reentrant phenomena beyond hybridization mechanisms.

cond-mat.dis-nn↗
arXiv2026

Optical Appearances of Accreting Ellis-Bronnikov Wormholes Observed from Both Sides of Throats

This study investigates the optical appearance of the Ellis-Bronnikov wormhole as viewed from both sides of its throat, under conditions of optically thick and thin accretion. By solving the geodesic equation, we derive the relationship between the impact parameter and the aiming distance of photons, and found that if the observer and the accretion disk are located on both sides of the throat, these two quantities are not equal. The optical image of the wormhole observed from the other side of the throat is obtained through the ray-tracing method. For optically thick accretion, increases in the parameter $n$ lead to an increase in the apparent size of the wormhole but a decrease in its brightness. For optically thin accretion, the image is similar to the internal and external inversion of the image observed from the other side. Furthermore, for optically thin accretion flows, the direct image does not block the emission from higher-order images, allowing radiation emitted from regions much closer to the event horizon to reach the observer. Our simulation results show that when the observer is on the $\mathcal{R}^+$ side, EB wormholes with small $n$ can mimic the images taken by the EHT to some extent, while wormholes with large $n$ or with the observer on the $\mathcal{R}^-$ side can be ruled out.

gr-qc↗
arXiv2026

Rigidity of Closed Minimal Hypersurfaces in $\mathbb{S}^5$

The celebrated Chern conjecture asserts that any closed minimal hypersurface in $\mathbb{S}^{n+1}$ with constant scalar curvature is isoparametric. In this paper, we resolve this conjecture in the affirmative for $M^4 \subset \mathbb S^5$ under the assumption that the Gauss-Kronecker curvature $K$ is constant. This result breaks the traditional reliance on consecutive trace conditions, demonstrating that the nonconsecutive spectral invariant set $\{H, S, K\}$ is sufficient to yield complete geometric rigidity. To overcome the analytical singular locus, we construct two novel weighted $3$-forms adapted to $S$ and $K$. Crucially, the global curvature estimates required to close our analysis are obtained unconditionally by proving the Euler characteristic $χ(M)=0$. This local-to-global approach provides a new paradigm for higher-dimensional rigidity problems.

math.DG↗
arXiv2026

Dissipative preparation of Laughlin-like states

Fractional quantum Hall (FQH) states are a central paradigm of strongly correlated quantum matter and a key platform for topological quantum computation. Here, we propose a purely dissipative protocol based on local loss and pump channels for preparing Laughlin-like states at filling $1/3$, with a possible extension to other $1/M$ filling states. We show that the Laughlin-like state is the exact unique steady state of the Lindbladian under open boundary conditions. Finite-size analysis of the Lindbladian gap suggests efficient dissipative preparation over the system sizes and parameter regime considered. We further demonstrate adiabatic pumping of a Laughlin-like state through slow modulation of the pump channels during the evolution. Our work opens a feasible route to preparing and manipulating FQH states on near-term quantum simulators.

quant-ph↗